Analysis of Simulated Hungarian Income Data

Introduction

This analysis examines simulated income data for Hungary, focusing on the relationships between income and various demographic factors including age, location, occupation, and gender. The dataset is simulated to reflect realistic patterns while maintaining a manageable size for analysis.

Data Simulation

The data was simulated by the data_simulation.R script. The data is available in the hungarian_income_data.csv file.

Important things to note about the generated data:

• Only the 8 most populated cities of Hungary are taken into count weighted by their population. List of cities: Budapest, Debrecen, Szeged, Miskolc, Pécs, Győr, Szombathely, Eger.

• Only the 10 most common occupations are taken into count weighted by their frequency in the workforce. List of occupations: Software Developer, Teacher, Doctor, Sales Representative, Engineer, Accountant, Nurse, Manager, Chef, Driver.

• The age distribution is generated by a beta distribution with parameters \(\alpha = 2\) and \(\beta = 3\) and multiplied by 95 to put the end result in the desired range. The beta distribution with the aforementioned parameters skews the age distribution towards younger ages, which is more realistic.

• There are three groups of people categorized by their age:

  • Underage: each person has a random age at which they start working between 14 and 24.

  • Working age: 19-67

  • Pension age: each person has a random retirement age between 60 and 75.

• Under 18 people have no income.

• Working age people have a regular income based on their age, occupation, city, and gender.

• Pension age people have a pension based on their occupation and city.

• All working age people are considered to be employed.

• The income of a working age man is 20.000 HUF higher than the income of a working age woman in the same occupation, city, and age group.

Demographics Analysis

library(forcats)

data <- data %>%
  mutate(age_group = cut(age, 
                        breaks = seq(0, 100, by = 2), 
                        right = FALSE, 
                        include.lowest = TRUE, 
                        labels = seq(0, 98, by = 2)))

dem_pyramid <- data %>%
  group_by(age_group, gender) %>%
  summarise(count = n(), .groups = 'drop') %>%
  mutate(count = ifelse(gender == "Male", -count, count))

ggplot(dem_pyramid, aes(x = age_group, y = count, fill = gender)) +
  geom_bar(stat = "identity", width = 0.8, color = "black") +
  scale_y_continuous(labels = abs, expand = expansion(mult = c(0.05, 0.05))) +
  scale_fill_manual(values = c("Male" = "#00BFFF", "Female" = "#FF3B3B")) +
  coord_flip() +
  labs(title = "Population Pyramid of Simulated Hungarian Data",
       x = "Age Group",
       y = "Count",
       fill = "Gender") +
  custom_theme +
  theme(legend.position = "top",
        axis.text.y = element_text(size = 10, face = "bold"),
        plot.margin = margin(t = 20, r = 20, b = 20, l = 20))

Data Preprocessing

We employ two steps to clean the data: inter quartile range (IQR) outlier detection, and clustering.

We use IQR outlier detection to reduce the noise of the data.

We employ K-Means clustering to find the three major demographics groups: unemployed/young, working age, retired.

After cleaning the data and finding the three demographic groups, we solely focus on the middle cluster, the working age people, as this study aims to analyze the income of the Hungarian population and it would be nonsensical to analyze unemployed people or retired people as if their pension was a salary.

detect_outliers <- function(x) {
  q1 <- quantile(x, 0.25)
  q3 <- quantile(x, 0.75)
  iqr <- q3 - q1
  lower_bound <- q1 - 1.5 * iqr
  upper_bound <- q3 + 1.5 * iqr
  return(x < lower_bound | x > upper_bound)
}

outliers <- detect_outliers(data$income)
data_clean <- data[!outliers, ]

set.seed(42)
income_age_matrix <- data_clean %>%
  select(income, age) %>%
  scale()

kmeans_result <- kmeans(income_age_matrix, centers = 3, nstart = 25)

data_clean$cluster <- kmeans_result$cluster

cluster_summary <- data_clean %>%
  group_by(cluster) %>%
  summarise(
    mean_income = mean(income),
    .groups = 'drop'
  ) %>%
  arrange(mean_income)

cluster_labels <- c("Working Age", "Pension Age", "Unemployed/Young")
data_clean$income_group <- factor(data_clean$cluster, 
                                 labels = cluster_labels[order(cluster_summary$mean_income)])

ggplot(data_clean, aes(x = age, y = income, color = income_group)) +
  geom_point(alpha = 0.5) +
  scale_color_viridis_d() +
  labs(title = "Age vs Income by Cluster",
       x = "Age",
       y = "Income (HUF)",
       color = "Income Group") +
  custom_theme

data <- data_clean %>%
  filter(income_group == "Working Age")

Descriptive Statistics

summary_stats <- summary(data)
kable(summary_stats, caption = "Summary Statistics of the Dataset (Outliers Removed)") %>%
  kable_styling(bootstrap_options = c("striped", "hover", "condensed"), full_width = FALSE)

Summary Statistics of the Dataset (Outliers Removed)

	age	city	occupation	gender	income	starting_age	retirement_age	age_group	cluster	income_group
	Min. :14.00	Length:7375	Length:7375	Length:7375	Min. :379808	Min. :14.00	Min. :60.00	30 : 397	Min. :1	Working Age :7375
	1st Qu.:29.00	Class :character	Class :character	Class :character	1st Qu.:529234	1st Qu.:18.00	1st Qu.:65.00	32 : 377	1st Qu.:1	Pension Age : 0
	Median :39.00	Mode :character	Mode :character	Mode :character	Median :579982	Median :19.00	Median :67.00	34 : 377	Median :1	Unemployed/Young: 0
	Mean :40.14	NA	NA	NA	Mean :582750	Mean :18.89	Mean :67.07	28 : 369	Mean :1	NA
	3rd Qu.:50.00	NA	NA	NA	3rd Qu.:632598	3rd Qu.:20.00	3rd Qu.:69.00	42 : 365	3rd Qu.:1	NA
	Max. :73.00	NA	NA	NA	Max. :864318	Max. :24.00	Max. :75.00	40 : 360	Max. :1	NA
	NA	NA	NA	NA	NA	NA	NA	(Other):5130	NA	NA

By the following income distribution plot, we can clearly see that on average a man has a higher income than a woman. This does not yet mean that given equal positions a man earns more money. However, it is indicative that we should further analyze this aspect of the data.

ggplot(data %>% filter(age >= 18), aes(x = income, fill = gender)) +
  geom_density(alpha = 0.6) +
  scale_fill_viridis_d() +
  labs(title = "Income Distribution by Gender",
       subtitle = "(working age only)",
       x = "Income (HUF)",
       y = "Density") +
  custom_theme

The following plot shows how the income is distributed against the age. An important thing to note is that as a person ages, their income increase. However it plateaus after a point, moreover, it even decreases in certain cases.

ggplot(data, aes(x = age, y = income, color = gender)) +
  geom_point(alpha = 0.1, width = 0.2) +
  scale_color_viridis_d() +
  labs(title = "Income Distribution by Age",
       subtitle = "(working age only)",
       x = "Age",
       y = "Income (HUF)") +
  custom_theme

To get a better grasp of how the income distribution is made up, we can split the data by occupation, giving us a new perspective into how certain occupation are more handsomely rewarded. We can see that Software Developers and Doctors have the highest income, compared to Sales Representatives who earn a lower income.

ggplot(data %>% filter(age >= 18), aes(x = reorder(occupation, income, FUN = median), y = income, color = occupation)) +
  geom_boxplot(alpha = 0.7) +
  geom_jitter(alpha = 0.1, width = 0.2) +
  scale_color_viridis_d() +
  coord_flip() +
  labs(title = "Income Distribution by Occupation",
       subtitle = "(working age only)",
       x = "Occupation",
       y = "Income (HUF)") +
  custom_theme

The following plot, like the one before, split the data. However, now we are analyzing how the city in which the person works at contributes to their salary. It is hard not to notice that the average person working in the capital, Budapest, enjoys a higher income compared to other cities.

ggplot(data %>% filter(age >= 18), aes(x = reorder(city, income, FUN = median), y = income, fill = city)) +
  geom_violin(alpha = 0.7) +
  geom_boxplot(width = 0.2, alpha = 0.5) +
  scale_fill_viridis_d() +
  coord_flip() +
  labs(title = "Income Distribution by City",
       subtitle = "(working age only)",
       x = "City",
       y = "Income (HUF)") +
  custom_theme

ggplot(data %>% filter(age >= 18), aes(x = age, y = income, color = gender)) +
  geom_point(alpha = 0.3) +
  geom_smooth(method = "loess", se = TRUE) +
  scale_color_viridis_d() +
  labs(title = "Relationship between Age and Income",
       subtitle = "(working age only)",
       x = "Age",
       y = "Income (HUF)") +
  custom_theme

income_by_category <- data %>%
  filter(age >= 18) %>%
  group_by(occupation, city, gender) %>%
  summarise(
    mean_income = mean(income),
    count = n(),
    .groups = 'drop'
  ) %>%
  arrange(desc(mean_income))

# heatmap
ggplot(income_by_category, aes(x = city, y = occupation, fill = mean_income)) +
  geom_tile() +
  scale_fill_viridis(name = "Mean Income (HUF)") +
  facet_wrap(~gender) +
  labs(title = "Mean Income by Occupation, City, and Gender",
       subtitle = "(working age only)",
       x = "City",
       y = "Occupation") +
  custom_theme +
  theme(axis.text.x = element_text(angle = 45, hjust = 1),
        strip.text = element_text(face = "bold"))

top_earners <- income_by_category %>%
  arrange(desc(mean_income)) %>%
  head(10)

kable(top_earners, 
      caption = "Top 10 Highest Earning Combinations",
      digits = 0) %>%
  kable_styling(bootstrap_options = c("striped", "hover", "condensed"), 
                full_width = FALSE)

Top 10 Highest Earning Combinations

occupation	city	gender	mean_income	count
Software Developer	Budapest	Male	745832	99
Software Developer	Budapest	Female	722333	100
Doctor	Budapest	Male	720956	61
Doctor	Budapest	Female	696091	77
Software Developer	Debrecen	Male	695662	43
Software Developer	Szeged	Male	694999	30
Manager	Budapest	Male	692519	107
Software Developer	Szeged	Female	684845	43
Engineer	Budapest	Male	675140	133
Software Developer	Debrecen	Female	674727	40

Hypothesis Testing

Parametric Tests

1. Gender Income Difference (t-test)

# Test if there's a significant difference in income between genders
t_test_result <- t.test(income ~ gender, data = data)
print(t_test_result)

## 
##  Welch Two Sample t-test
## 
## data:  income by gender
## t = -10.655, df = 7367.6, p-value < 2.2e-16
## alternative hypothesis: true difference in means between group Female and group Male is not equal to 0
## 95 percent confidence interval:
##  -21472.33 -14798.96
## sample estimates:
## mean in group Female   mean in group Male 
##             573558.0             591693.7

2. City Income Differences (ANOVA)

# Test if there are significant differences in income between cities
city_anova <- aov(income ~ city, data = data)
print(summary(city_anova))

##               Df    Sum Sq   Mean Sq F value Pr(>F)    
## city           7 1.356e+13 1.937e+12   539.9 <2e-16 ***
## Residuals   7367 2.643e+13 3.587e+09                   
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

3. Occupation Income Differences (ANOVA)

# Test if there are significant differences in income between occupations
occupation_anova <- aov(income ~ occupation, data = data)
print(summary(occupation_anova))

##               Df    Sum Sq   Mean Sq F value Pr(>F)    
## occupation     9 1.545e+13 1.717e+12   515.5 <2e-16 ***
## Residuals   7365 2.453e+13 3.331e+09                   
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Non-parametric Tests

1. Gender Income Distribution (Kolmogorov-Smirnov Test)

# Test if income distributions differ between genders
male_income <- data$income[data$gender == "Male"]
female_income <- data$income[data$gender == "Female"]
ks_test <- ks.test(male_income, female_income)
print(ks_test)

## 
##  Asymptotic two-sample Kolmogorov-Smirnov test
## 
## data:  male_income and female_income
## D = 0.10969, p-value < 2.2e-16
## alternative hypothesis: two-sided

2. Income Distribution by City (Kruskal-Wallis Test)

# Test if income distributions differ between cities
kruskal_test <- kruskal.test(income ~ city, data = data)
print(kruskal_test)

## 
##  Kruskal-Wallis rank sum test
## 
## data:  income by city
## Kruskal-Wallis chi-squared = 2574.8, df = 7, p-value < 2.2e-16

Regression Analysis

Multiple Linear Regression

# Convert categorical variables to factors
data_working_age <- data %>% filter(age >= 18)
data_working_age$city <- as.factor(data_working_age$city)
data_working_age$occupation <- as.factor(data_working_age$occupation)
data_working_age$gender <- as.factor(data_working_age$gender)

# Fit the model
model1 <- lm(income ~ age + I(age^2) + city + occupation + gender, data = data_working_age)

# Create a beautiful model summary table
model_summary <- summary(model1)
kable(tidy(model_summary), caption = "Multiple Linear Regression Results (Working Age Only)") %>%
  kable_styling(bootstrap_options = c("striped", "hover", "condensed"), full_width = FALSE)

Multiple Linear Regression Results (Working Age Only)

term	estimate	std.error	statistic	p.value
(Intercept)	434779.5680	3042.221285	142.91517	0
age	8492.2241	146.411115	58.00259	0
I(age^2)	-79.2278	1.733131	-45.71369	0
cityDebrecen	-50118.9464	885.873428	-56.57574	0
cityEger	-100673.3825	1223.767096	-82.26515	0
cityGyőr	-98817.6008	1136.347121	-86.96075	0
cityMiskolc	-99847.5434	1064.213618	-93.82284	0
cityPécs	-98754.3471	1121.335644	-88.06850	0
citySzeged	-49284.8644	997.322532	-49.41718	0
citySzombathely	-99991.5016	1348.646293	-74.14212	0
occupationChef	-42406.4096	1535.934166	-27.60952	0
occupationDoctor	59742.5726	1554.207886	38.43924	0
occupationDriver	-50964.5811	1535.962289	-33.18088	0
occupationEngineer	18051.4651	1251.405236	14.42496	0
occupationManager	37826.6478	1351.616466	27.98623	0
occupationNurse	-32257.7329	1187.823213	-27.15702	0
occupationSales Representative	-60650.2152	1057.574345	-57.34842	0
occupationSoftware Developer	88380.8010	1335.055549	66.20009	0
occupationTeacher	-22511.0613	1128.935083	-19.94008	0
genderMale	18870.6304	582.757427	32.38162	0

# Enhanced model diagnostics
par(mfrow = c(2,2))
plot(model1, col = income_palette[1], pch = 19, cex = 0.7)

Polynomial Regression for Age-Income Relationship

# Fit polynomial regression
model2 <- lm(income ~ poly(age, 3), data = data_working_age)

# Create a static plot
ggplot(data_working_age, aes(x = age, y = income)) +
  geom_point(alpha = 0.1, color = income_palette[1]) +
  geom_smooth(method = "lm", formula = y ~ poly(x, 3), 
              color = income_palette[5], fill = income_palette[5], alpha = 0.2) +
  labs(title = "Polynomial Regression: Age vs Income",
       subtitle = "Cubic polynomial fit with confidence interval (Working Age Only)",
       x = "Age",
       y = "Income (HUF)") +
  custom_theme

# Model comparison
model_comparison <- data.frame(
  Model = c("Multiple Linear", "Polynomial"),
  R_squared = c(summary(model1)$r.squared, summary(model2)$r.squared),
  Adj_R_squared = c(summary(model1)$adj.r.squared, summary(model2)$adj.r.squared)
)

kable(model_comparison, caption = "Model Comparison (Working Age Only)") %>%
  kable_styling(bootstrap_options = c("striped", "hover", "condensed"), full_width = FALSE)

Model Comparison (Working Age Only)

Model	R_squared	Adj_R_squared
Multiple Linear	0.8851681	0.8848691
Polynomial	0.1541084	0.1537614

Predictions

What is the predicted income of a 35 years old male software developer working in Budapest?

# Create a sample prediction
new_data <- data.frame(
  age = 35,
  city = "Budapest",
  occupation = "Software Developer",
  gender = "Male"
)

# Predict income
prediction <- predict(model1, newdata = new_data, interval = "prediction")
print(prediction[1])

## [1] 742204.8

Summary and Conclusions

The analysis of the simulated Hungarian income data reveals several interesting patterns:

1. There is a significant gender pay gap, with males earning more on average than females.
2. Income varies significantly across different cities, with Budapest showing the highest average income.
3. Occupation has a strong effect on income, with software developers and doctors earning the most.
4. Age shows a non-linear relationship with income, peaking in the middle age range.
5. The regression models explain a significant portion of the income variation.

The analysis demonstrates the complex interplay between various factors affecting income levels in Hungary. The findings suggest that both demographic factors (age, gender) and professional factors (occupation, location) play important roles in determining income levels.